A Data-adaptive Trace Abstraction Approach to the Prediction of Business Process Performances

This paper presents a novel approach to the discovery of predictive process model a meant to support the run-time prediction of some performance indicator (e.g., the remaining processing time on new ongoing process instances. To this purpose, WC combine a scries of data mining techniques (ranging from pattern mining, to non-parametric regression and to predictive clustering) with ad-hoc data transformation and abstraction mechanisms. As a result, a modular representation of the process is obtained, where different performance-relevant variants of it are provided with separate regression models, and discriminated on the basis of context information. Notably, the approach is capable to look at the given log traces at a proper level of abstraction, in a pretty automatic and transparent fashion, which reduces the need for heavy intervention by the analyst (which is, indeed, a major draw;back of previous solutions in the literature). The approach has been validated on a real application scenario, with satisfacto results, in terms of both prediction accuracy and robustness.